A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications.

RNA sequencing (RNA-seq) is a genomic approach for the detection and quantitative analysis of messenger RNA molecules in a biological sample and is useful for studying cellular responses. RNA-seq has fueled much discovery and innovation in medicine over recent years. For practical reasons, the technique is usually conducted on samples comprising thousands to millions of cells. However, this has hindered direct assessment of the fundamental unit of biology-the cell. Since the first single-cell RNA-sequencing (scRNA-seq) study was published in 2009, many more have been conducted, mostly by specialist laboratories with unique skills in wet-lab single-cell genomics, bioinformatics, and computation. However, with the increasing commercial availability of scRNA-seq platforms, and the rapid ongoing maturation of bioinformatics approaches, a point has been reached where any biomedical researcher or clinician can use scRNA-seq to make exciting discoveries. In this review, we present a practical guide to help researchers design their first scRNA-seq studies, including introductory information on experimental hardware, protocol choice, quality control, data analysis and biological interpretation

Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors.

Large-scale single-cell RNA sequencing (scRNA-seq) data sets that are produced in different laboratories and at different times contain batch effects that may compromise the integration and interpretation of the data. Existing scRNA-seq analysis methods incorrectly assume that the composition of cell populations is either known or identical across batches. We present a strategy for batch correction based on the detection of mutual nearest neighbors (MNNs) in the high-dimensional expression space. Our approach does not rely on predefined or equal population compositions across batches; instead, it requires only that a subset of the population be shared between batches. We demonstrate the superiority of our approach compared with existing methods by using both simulated and real scRNA-seq data sets. Using multiple droplet-based scRNA-seq data sets, we demonstrate that our MNN batch-effect-correction method can be scaled to large numbers of cells

Longitudinal multiparameter single-cell analysis of macaques immunized with pneumococcal protein-conjugated or unconjugated polysaccharide vaccines reveals distinct antigen specific memory B cell repertoires

<div><p>Background</p><p>The efficacy of protein-conjugated pneumococcal polysaccharide vaccines has been well characterized for children. The level of protection conferred by unconjugated polysaccharide vaccines remains less clear, particularly for elderly individuals who have had prior antigenic experience through immunization with unconjugated polysaccharide vaccines or natural exposure to <i>Streptococcus pneumoniae</i>.</p><p>Methods</p><p>We compared the magnitude, diversity and genetic biases of antigen-specific memory B cells in two groups of adult cynomolgus macaques that were immunized with a 7-valent conjugated vaccine and boosted after five years with either a 13-valent pneumococcal polysaccharide conjugate vaccine (13vPnC) or a 23-valent unconjugated pneumococcal polysaccharide vaccine (23vPS) using microengraving (a single-cell analysis method) and single-cell RT-PCR.</p><p>Results</p><p>Seven days after boosting, the mean frequency of antigen-specific memory B cells was significantly increased in macaques vaccinated with 13vPnC compared to those receiving 23vPS. The 13vPnC-vaccinated macaques also exhibited a more even distribution of antibody specificities to four polysaccharides in the vaccine (PS4, 6B, 14, 23F) that were examined. However, single-cell analysis of the antibody variable region sequences from antigen-specific B cells elicited by unconjugated and conjugated vaccines indicated that both the germline gene segments forming the heavy chains and the average lengths of the Complementary Determining Region 3 (CDR3) were similar.</p><p>Conclusions</p><p>Our results confirm that distinctive differences can manifest between antigen-specific memory B cell repertoires in nonhuman primates immunized with conjugated and unconjugated pneumococcal polysaccharide vaccines. The study also supports the notion that the conjugated vaccines have a favorable profile in terms of both the frequency and breadth of the anamnestic response among antigen-specific memory B cells.</p></div